Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "262" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460010 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460008 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460007 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | dish_maintenance | 0.00% | 0.00% | 0.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5623 | 0.5557 | 0.3301 | nan | nan |
| 2459998 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.868388 | 12.415378 | 4.870300 | 4.994016 | -0.317941 | -0.173125 | 0.277357 | 3.381003 | 0.5446 | 0.5561 | 0.3837 | nan | nan |
| 2459997 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.754428 | 13.389675 | 5.108052 | 5.409533 | -0.444141 | -0.368224 | 0.461755 | 3.563399 | 0.5605 | 0.5718 | 0.3860 | nan | nan |
| 2459996 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.511412 | 14.673028 | 6.484462 | 6.671431 | -0.049036 | -0.307070 | 0.610957 | 1.600119 | 0.5710 | 0.5781 | 0.3987 | nan | nan |
| 2459995 | dish_maintenance | 100.00% | 0.86% | 9.73% | 0.00% | - | - | 12.584101 | 13.658502 | 5.968844 | 6.360498 | 2.979786 | 4.095472 | 0.413722 | 2.878943 | 0.2776 | 0.2580 | 0.1240 | nan | nan |
| 2459994 | dish_maintenance | 100.00% | 2.05% | 11.88% | 0.00% | - | - | 11.925018 | 13.194439 | 5.280098 | 5.685274 | 3.757548 | 5.143888 | -0.057387 | 1.599418 | 0.2751 | 0.2531 | 0.1253 | nan | nan |
| 2459993 | dish_maintenance | 100.00% | 44.64% | 70.93% | 0.00% | - | - | 14.457151 | 14.796965 | 4.980980 | 5.208923 | 4.981514 | 7.449967 | 0.453805 | 3.177864 | 0.2209 | 0.2037 | 0.1117 | nan | nan |
| 2459991 | dish_maintenance | 100.00% | 0.98% | 13.86% | 0.00% | - | - | 13.961361 | 15.366612 | 5.197997 | 5.498678 | 3.975100 | 5.928965 | 0.167858 | 2.276379 | 0.2707 | 0.2478 | 0.1260 | nan | nan |
| 2459990 | dish_maintenance | 100.00% | 4.86% | 16.95% | 0.00% | - | - | 11.505905 | 12.573736 | 5.113746 | 5.286672 | 4.004272 | 5.590524 | 0.475438 | 2.218833 | 0.2709 | 0.2495 | 0.1249 | nan | nan |
| 2459989 | dish_maintenance | 100.00% | 2.65% | 11.08% | 0.00% | - | - | 10.979458 | 12.624446 | 4.537044 | 4.932626 | 4.051434 | 4.283595 | -0.026379 | 1.578777 | 0.2700 | 0.2490 | 0.1256 | nan | nan |
| 2459988 | dish_maintenance | 100.00% | 2.65% | 12.92% | 0.00% | - | - | 12.837710 | 14.891861 | 5.297796 | 5.395634 | 4.919771 | 7.122904 | 0.058462 | 1.681330 | 0.2729 | 0.2528 | 0.1251 | nan | nan |
| 2459987 | dish_maintenance | 100.00% | 1.24% | 5.46% | 0.00% | - | - | 10.310900 | 12.274842 | 5.110630 | 5.446165 | 3.497036 | 3.778188 | 0.013949 | 2.299935 | 0.2811 | 0.2630 | 0.1234 | nan | nan |
| 2459986 | dish_maintenance | 100.00% | 0.05% | 2.70% | 0.00% | - | - | 13.088404 | 14.779207 | 5.602191 | 5.791840 | 5.114073 | 5.885085 | 2.505821 | 7.329627 | 0.3133 | 0.2988 | 0.1178 | nan | nan |
| 2459985 | dish_maintenance | 100.00% | 0.11% | 1.89% | 0.00% | - | - | 12.742170 | 13.628475 | 5.132571 | 5.386861 | 3.622211 | 4.172299 | 0.590611 | 3.214265 | 0.2728 | 0.2589 | 0.1228 | nan | nan |
| 2459984 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.679913 | 7.915764 | 9.631536 | 10.411977 | 9.310335 | 12.700458 | 13.431223 | 25.512787 | 0.0346 | 0.0287 | 0.0053 | nan | nan |
| 2459983 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.625194 | 7.713119 | 9.025929 | 10.036022 | 9.059358 | 10.729665 | 8.477843 | 25.604136 | 0.0333 | 0.0281 | 0.0047 | nan | nan |
| 2459982 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 4.323363 | 5.112259 | 7.519501 | 8.299689 | 4.205052 | 4.846106 | 2.788200 | 4.495607 | 0.0323 | 0.0273 | 0.0044 | nan | nan |
| 2459981 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.254513 | 7.751807 | 9.618104 | 10.052120 | 9.927571 | 11.852543 | 11.261069 | 18.021786 | 0.0342 | 0.0288 | 0.0048 | nan | nan |
| 2459980 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 5.755564 | 6.980545 | 8.695729 | 9.498255 | 8.717184 | 11.041066 | 7.011176 | 9.660856 | 0.0333 | 0.0283 | 0.0043 | nan | nan |
| 2459979 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.493217 | 7.450041 | 8.049179 | 9.060161 | 8.782189 | 9.803271 | 10.338362 | 23.544947 | 0.0334 | 0.0273 | 0.0041 | nan | nan |
| 2459978 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.564281 | 7.581552 | 8.648014 | 9.821885 | 8.970956 | 10.421298 | 11.688471 | 29.176525 | 0.0302 | 0.0263 | 0.0035 | nan | nan |
| 2459977 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.865643 | 7.720728 | 8.796314 | 9.353767 | 9.564099 | 10.550789 | 19.013684 | 18.926326 | 0.0338 | 0.0283 | 0.0051 | nan | nan |
| 2459976 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.633953 | 8.135784 | 9.179790 | 9.470799 | 9.063579 | 9.937940 | 13.471687 | 13.356628 | 0.0310 | 0.0275 | 0.0032 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 12.415378 | 9.868388 | 12.415378 | 4.870300 | 4.994016 | -0.317941 | -0.173125 | 0.277357 | 3.381003 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 13.389675 | 10.754428 | 13.389675 | 5.108052 | 5.409533 | -0.444141 | -0.368224 | 0.461755 | 3.563399 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 14.673028 | 11.511412 | 14.673028 | 6.484462 | 6.671431 | -0.049036 | -0.307070 | 0.610957 | 1.600119 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 13.658502 | 12.584101 | 13.658502 | 5.968844 | 6.360498 | 2.979786 | 4.095472 | 0.413722 | 2.878943 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 13.194439 | 11.925018 | 13.194439 | 5.280098 | 5.685274 | 3.757548 | 5.143888 | -0.057387 | 1.599418 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 14.796965 | 14.457151 | 14.796965 | 4.980980 | 5.208923 | 4.981514 | 7.449967 | 0.453805 | 3.177864 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 15.366612 | 13.961361 | 15.366612 | 5.197997 | 5.498678 | 3.975100 | 5.928965 | 0.167858 | 2.276379 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 12.573736 | 12.573736 | 11.505905 | 5.286672 | 5.113746 | 5.590524 | 4.004272 | 2.218833 | 0.475438 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 12.624446 | 12.624446 | 10.979458 | 4.932626 | 4.537044 | 4.283595 | 4.051434 | 1.578777 | -0.026379 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 14.891861 | 14.891861 | 12.837710 | 5.395634 | 5.297796 | 7.122904 | 4.919771 | 1.681330 | 0.058462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 12.274842 | 10.310900 | 12.274842 | 5.110630 | 5.446165 | 3.497036 | 3.778188 | 0.013949 | 2.299935 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 14.779207 | 14.779207 | 13.088404 | 5.791840 | 5.602191 | 5.885085 | 5.114073 | 7.329627 | 2.505821 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Shape | 13.628475 | 13.628475 | 12.742170 | 5.386861 | 5.132571 | 4.172299 | 3.622211 | 3.214265 | 0.590611 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Discontinuties | 25.512787 | 6.679913 | 7.915764 | 9.631536 | 10.411977 | 9.310335 | 12.700458 | 13.431223 | 25.512787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Discontinuties | 25.604136 | 6.625194 | 7.713119 | 9.025929 | 10.036022 | 9.059358 | 10.729665 | 8.477843 | 25.604136 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Power | 8.299689 | 4.323363 | 5.112259 | 7.519501 | 8.299689 | 4.205052 | 4.846106 | 2.788200 | 4.495607 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Discontinuties | 18.021786 | 7.751807 | 6.254513 | 10.052120 | 9.618104 | 11.852543 | 9.927571 | 18.021786 | 11.261069 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Variability | 11.041066 | 6.980545 | 5.755564 | 9.498255 | 8.695729 | 11.041066 | 8.717184 | 9.660856 | 7.011176 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Discontinuties | 23.544947 | 6.493217 | 7.450041 | 8.049179 | 9.060161 | 8.782189 | 9.803271 | 10.338362 | 23.544947 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | nn Temporal Discontinuties | 29.176525 | 7.581552 | 6.564281 | 9.821885 | 8.648014 | 10.421298 | 8.970956 | 29.176525 | 11.688471 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Temporal Discontinuties | 19.013684 | 6.865643 | 7.720728 | 8.796314 | 9.353767 | 9.564099 | 10.550789 | 19.013684 | 18.926326 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 262 | N20 | dish_maintenance | ee Temporal Discontinuties | 13.471687 | 8.135784 | 6.633953 | 9.470799 | 9.179790 | 9.937940 | 9.063579 | 13.356628 | 13.471687 |